Build a model from a prompt or a document

System Two from a task description, a policy PDF, or both. Portable definitions, question contracts and sample inputs.

When you have a policy rather than a list of questions, start from the policy.

From a prompt

curl https://api.aityx.ai/v1/systemtwo/models \
  -H "Authorization: Bearer $AITYX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "aityx-gen-0.98",
    "prompt": "Decide whether an expense claim can be auto-approved, needs a manager, or must be rejected. Policy, in this order: reject if the total exceeds USD 75 and a receipt is missing; otherwise route to a manager if cost per person exceeds USD 75, total exceeds USD 500, or submission is more than 30 calendar days after the expense date; otherwise auto-approve. Calculate cost per person from the total and number of people. Require a positive total and at least one person.",
    "output": {
      "disposition": {
        "type": "choice",
        "instructions": "What should happen to the claim?",
        "criteria": { "auto_approve": "", "manager": "", "reject": "" }
      },
      "cost_per_person": { "type": "number", "instructions": "Expense per person in USD" }
    }
  }'

The response includes model: {content, questions} and the derived input schema. Its top-level content and output repeat the definition and question contract for authoring. Without output, the generator designs the questions as well. Save the complete model object to submit with later execution requests.

From documents

Send multipart/form-data with the fields as form parts and documents as file parts. PDFs and images are read natively; spreadsheets, Word, PowerPoint and text files are extracted to text.

curl https://api.aityx.ai/v1/systemtwo/models \
  -H "Authorization: Bearer $AITYX_API_KEY" \
  -F model=aityx-gen-0.98 \
  -F mode=thinking \
  -F 'prompt=Qualify rental income for a mortgage application under the attached policy.' \
  -F file=@rental-income-policy.pdf

The generator is instructed to use the documents as the source of truth. Review the resulting rules and their why annotations against the policy, then test boundaries and exceptions. Structural validation does not establish that a generated rule interprets the policy correctly. Use mode: thinking for long or dense documents.

Revise the definition you kept

Every generated definition is returned for you to inspect and keep. To revise it, send its content and questions as output to the same endpoint, with a new prompt. The revised definition is returned without replacing the file your application uses. See Compare revised rules.

Sample inputs

Ask for them in the prompt (“create six demo cases covering the boundaries”) and the response carries an examples array of named JSON states that satisfy the model’s inputs. They are fictional, validated against the model, and carry no predicted answers.

Then

Execute with POST /v1/systemone, sending the complete returned model object and a state.
Upload it in the console to see the diagram, tables and input schema. Download your work before leaving the session.
Revise when the policy changes.